Finite electro-elasticity with physics-augmented neural networks
type: Item
instance of: scholarly article
title: Finite electro-elasticity with physics-augmented neural networks
author: Dominik Klein
author: Jesús Martínez-Frutos
author: Oliver Weeger
author: Rogelio Ortigosa
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2022-10-10T00:00:00Z
full work available at URL: https://arxiv.org/abs/2206.05139
Mathematics Subject Classification ID: 74F15
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 74E30
zbMATH DE Number: 7598913
zbMATH Keywords: nonlinear electro-elasticity
zbMATH Keywords: constitutive modeling
zbMATH Keywords: physics-augmented machine learning
zbMATH Keywords: electro-active polymers
zbMATH Keywords: homogenization
MaRDI profile type: Publication
OpenAlex ID: W4293155360
cites work: Electromechanical instabilities in fiber-constrained, dielectric-elastomer composites subjected to all-around dead-loading
cites work: Fiber-constrained, dielectric-elastomer composites: finite-strain response and stability analysis
cites work: Instabilities in multilayered soft dielectrics
cites work: Two-scale computational homogenization of electro-elasticity at finite strains
cites work: Nonlinear electroelasticity
cites work: Ordinary and strong ellipticity in the equilibrium theory of incompressible hyperelastic solids
cites work: Q3126791
cites work: A variational approach for materially stable anisotropic hyperelasticity
cites work: Convexity conditions and existence theorems in nonlinear elasticity
cites work: Q4155912
cites work: A mixed variational framework for the design of energy-momentum schemes inspired by the structure of polyconvex stored energy functions
cites work: Invariant formulation of hyperelastic transverse isotropy based on polyconvex free energy functions.
cites work: A new framework for large strain electromechanics based on convex multi-variable strain energies: variational formulation and material characterisation
cites work: A computational framework for large strain nearly and truly incompressible electromechanics based on convex multi-variable strain energies
cites work: A variational approach to nonlinear electro-magneto-elasticity: Convexity conditions and existence theorems
cites work: Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
cites work: Geometry of logarithmic strain measures in solid mechanics
cites work: The axiomatic introduction of arbitrary strain tensors by Hans Richter – a commented translation of ‘Strain tensor, strain deviator and stress tensor for finite deformations’
cites work: Fitting hyperelastic models to experimental data
cites work: Unsupervised discovery of interpretable hyperelastic constitutive laws
cites work: Data-driven computational mechanics
cites work: Data-driven fracture mechanics
cites work: Deep Learning in Computational Mechanics
cites work: Neural Networks and Deep Learning
cites work: What Machine Learning Can Do for Computational Solid Mechanics
cites work: Q4023641
cites work: Q4158542
cites work: Q5691468
cites work: Tensor algebra and tensor analysis for engineers. With applications to continuum mechanics
cites work: Nonlinear solid mechanics. A continuum approach for engineering
cites work: Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning
cites work: A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling
cites work: Machine learning strategies for systems with invariance properties
cites work: Anisotropic hyperelastic constitutive models for finite deformations combining material theory and data-driven approaches with application to cubic lattice metamaterials
cites work: Constrained neural network training and its application to hyperelastic material modeling
cites work: Electrostatic Forces and Stored Energy for Deformable Dielectric Materials
cites work: Multi-resolution methods for the topology optimization of nonlinear electro-active polymers at large strains
cites work: Mathematical Methods in Continuum Mechanics of Solids
cites work: Nonlinear electroelastostatics: a variational framework
cites work: Nonlinear electroelastic deformations
cites work: Mathematical formulations for elastic magneto-electrically coupled soft materials at finite strains: time-independent processes
cites work: Numerical modelling of non-linear electroelasticity
cites work: On the spatial and material motion problems in nonlinear electro-elastostatics with consideration of free space
cites work: Q4537382
cites work: Approximation of anisotropic elasticity tensors at the reference state with polyconvex energies
cites work: Anisotropic polyconvex energies on the basis of crystallographic motivated structural tensors
cites work: Theory of representations for tensor functions. -- A unified invariant approach to constitutive equations
cites work: Polyconvexity of generalized polynomial-type hyperelastic strain energy functions for near-incompressibility.
cites work: On the physical assumptions underlying the volumetric-isochoric split and the case of anisotropy
cites work: Q3514988
cites work: Tensors which characterize anisotropies
cites work: On the canonical representations for Kronecker powers of orthogonal tensors with application to material symmetry problems
cites work: Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials
cites work: Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity
cites work: Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
cites work: Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
cites work: Hyperelastic models for rubber-like materials: consistent tangent operators and suitability for Treloar's data
cites work: A convex multi-variable based computational framework for multilayered electro-active polymers
cites work: Homogenization and multiscale stability analysis in finite magneto-electro-elasticity. Application to soft matter EE, ME and MEE composites

Finite electro-elasticity with physics-augmented neural networks (cites work)
cites work: A new framework for large strain electromechanics based on convex multi-variable strain energies: finite element discretisation and computational implementation

Finite electro-elasticity with physics-augmented neural networks (DOI)
DOI: 10.1016/J.CMA.2022.115501